Summer 2024 Workshops

LATIS offers a series of workshops that are free and open to all faculty, graduate students, and staff. Join our LATIS Research Workshops Google Group to be the first to learn about workshops. You can view the videos, slides, and materials from past workshops at the LATIS Workshop Materials website.

Workshops are also offered on even more topics from partner departments:

• Research Data Services (UMN Libraries)
• MSI (MN Supercomputing Institute)

See workshops.umn.edu for a list of current Research and Computing Workshops across the University. 

Summer 2024 LATIS Workshop Schedule

Workshops will be a mix of in-person and online formats. Click on the links below for a detailed description of each workshop. 

Register here for one or more workshops! 

Summer R Workshop Series

R is a popular tool for data analysis and statistical computing, and is a great alternative to tools like SPSS, Stata, or Excel. Additionally, R is free and designed for reproducible research. This workshop series will teach you how to get started using R to clean, manipulate, summarize,  and visualize data. We will not cover statistical analysis. Rather, this series will focus on all the steps that come *before* you run statistics, because getting your data into the right format is often the hardest part of data analysis. 

While these workshops are open to participants from all disciplines, we will focus on issues social and behavioral scientists often encounter when using data in R. You can sign up for all five workshops in the series, or attend just one. 

Each workshop will have the following format:

• Asynchronous materials to review before the workshop
• 10:00am - 10:10am: Open help session/time to review materials
• 10:10am - 11:40am: Live Demonstration
• 11:40am - Noon: Open help session/time to work on activities

The final "Putting it all together: Putting it all together: activities, data, & donuts" workshop will be held in person. There will be snacks and activities to help you practice and synthesize all the skills you have learned using real data. Feel free to bring your own data and R questions to this workshop as well. 

Register today! 

Asynchronous Workshops

We also offer asynchronous workshops in canvas that you can take at your own pace. Please contact us [email protected] with any questions or trouble enrolling. Click on the links below for a detailed description of each workshop.

Workshop Descriptions

June 26: Introduction to R

This workshop will teach you how to get started using R to explore and clean your data. 

You will learn how to: 

• Create an R script (syntax/command file) to capture data cleaning steps in a reproducible way

• Load a comma-delimited spreadsheet (.csv) into R as a dataset
• View and examine data in R 
• Check and correct missing values, rename variables, create new variables, and recode values in the data 
• Save cleaned data file in formats for later use in R or other applications

July 3: Manipulating data using dplyr

This workshop will introduce you to the dplyr package designed for data manipulation in R. 

You will learn how to: 

• Subset a dataset to select the column/variables you need
• Filter rows of the dataset to include only certain cases
• Sort data by values in a column/variable
• Chain together multiple R functions in a single command
• Group and summarize data using descriptive statistics

July 10: Reshaping and merging data

Often, data are not in the shape or format you need for analysis or graphing. This session will teach you how to reshape data using the tidyr package and how to merge multiple files into a single dataset. 

You will learn how to:

• Transform data from "wide" (single row per individual case; many columns) to "long" (multiple rows per individual case; fewer columns) and vice versa. 
• Check for and remove duplicates in a file before merging. 
• Examine cases that are present in each dataset before merging and determine the overlap
• Learn about and perform different types of merges depending on what cases you want to keep in your final dataset. 

July 17: Visualizing data with Ggplot2

Ggplot2 is a popular package that extends R’s capability for data visualization, allowing users to produce attractive and complex graphics in a relatively simple way. This workshop will introduce the logic behind ggplot2 and demonstrate how to create data visualizations using this package. 

You will learn how to:

• Understand the basics of the "grammar of graphics" underlying ggplot2's functionality
• Create a variety of reproducible data visualizations in R, such as histograms, line charts, scatter plots, and more 
• Visualize data by groups in multiple ways, including color labeling and faceting 

July 24: Putting it all together: Activities, data, & donuts

Join us in person to put all the covered R tools together to work through your data questions and needs in R. There will be structured activities for you to work through using data, opportunities to get help from R experts, and to ask questions about your own data or code. And in case practicing your R skills isn't sweet enough, there will also be donuts.  

You will learn how to: 

• Approach a problem in R and decide what needs to be done
• Apply methods for aggregating, reshaping, visualizing, and "wrangling" data learned in previous workshops
• Check work to ensure R did what you expected
• Search for other resources to solve R problems 

Managing Data When you Graduate (Canvas Modules)

Research and creative work doesn't end with degree completion; however, access to many of the data storage tools and software that have supported that work changes when students become alumni. This asynchronous workshop will help graduate students navigate questions about whether they can take their data and materials with them when they leave the university, and if so, how to do it. This workshop is co-organized by the University Libraries. 

The workshop will cover:

• The University policies that guide ownership of data
• Access changes to storage, software, and services that happen upon graduation 
• Strategies and tips for ensuring data are accessible and understandable long after graduation

Schedule a consultation to discuss:

• How to make a plan to ensure a smooth transition for your data and materials between graduate school and your next endeavor
• Specific advice and troubleshooting for your own research and situation. 

To be successful, you should:

• Be a graduate student at the University of Minnesota at least a year into your program (it never hurts to plan early!), or who is nearing the end of your program. 
• Have a research project (part of a dissertation or thesis) that has generated data or materials that you want to keep track of after you leave. This can include collaborative projects that will continue at UMN after graduation.

Introduction to Survey Sampling (Canvas Modules)

This is an interactive, self-paced Canvas course, designed for those who are either 1) completely new to surveying or 2) have never had formal instruction in survey/sampling design. By the end of course, you should be able to: 

1. Differentiate between a census and a sample
2. Describe features and limitations of common sampling methods
3. Recognize different sources of survey error/bias
4. Describe how different sources of survey error/bias affects the conclusions you can draw with your survey

This brief, introductory course to sampling is designed to take around 1-3 hours to complete, depending on the material you choose to engage with.

Qualtrics Tutorials (Canvas Modules)

We have three asynchronous Canvas courses available for you to take: 

1. Introduction to Qualtrics: Are you brand new to using Qualtrics? Or has it been a really long time since you used Qualtrics? Start here to learn the ropes. [Expected time: 1 hour]
    
2. Qualtrics Data Integrity & Management: No matter if you are new to Qualtrics or a long-time user, this module is a must for any Qualtrics user who is interested in 1) how to make Qualtrics data more readable and suitable to their needs, 2) best practices for conducting reproducible research within Qualtrics (e.g., sharing and archiving survey information, how to export data reproducibly, etc.). [Expected time: 35-45 minutes]
    
3. Designing Experiments & Complex Surveys in Qualtrics: Sometimes figuring out the right bells and whistles for more complex research designs in Qualtrics can be daunting. If you’re looking to build complex surveys or experimental tasks within Qualtrics, this tutorial is for you! We cover how to use some more complex functionality within Qualtrics, such as the using the survey flow, branching logic, embedded data, embedded media, piped text, “loop & merge”, integration with MTurk/Prolific, and more! In this module, you will watch a video walkthrough from our Fall 2021 workshop. [Expected time: 10-20 minutes for Canvas content; 2 hours of video content]

Working with Data in R - Tutorials (Canvas Modules)

R is a popular tool for data analysis and statistical computing, and is a great alternative to tools like SPSS, Stata, or Excel. R is designed for reproducible research and can be used for many parts of the research process besides statistical analysis. This asynchronous course includes introductory readings, videos, and activities to build on and advance your data skills in R. 

Topics include

1. Foundations in R: Just starting in R? Welcome! This module will walk you through the basics of R and set the foundation for the more advanced modules below. 
2. Publication worthy graphs with ggplot2: Learn how to adjust colors, axises, legends, and themes, as well as how to reproducibility save graphs for publication. 
3. Create a table using dplyr: Learn how to aggregate data and create summaries for tables for publication. 
4. Reshaping data: Data are not always in the right format for analysis or visualization. Learn how to transform data from wide to long format and back again. 
5. R Markdown: Combine code, output, and text into readable documents with R Markdown. Learn how to create a basic R markdown document for research. 
6. Working with Qualtrics data in R: Qualtrics is a popular tool for survey research, but the resulting data often require cleaning before analyzing in R. Learn how to efficiently clean Qualtrics data for use in R, including how to reproducibly remove the multiple headers, save labels, and combine multi-response columns. 

Linux for Research Computing (Canvas Modules)

This asynchronous course is a gentle introduction to command line programming using Linux. It is designed for CLA researchers and students who need to use high performance computing resources for their work (for example, to run fMRI analyses, parallel computing, or large scale analyses), but have little to no experience with Linux. 

This course guides participants through:

1. Connecting to the CLA compute cluster
2. Navigating directory and file structure using the Linux command-line terminal
3. Creating, modifying, and moving files using the Linux command-line terminal
4. Submitting an interactive and a batch computing job and understanding when it is beneficial to use one or the other